Biostatistics-Scalable Statistical Inference for Massive Health Data: Challenges and Opportunities
题目：Scalable Statistical Inference for Massive Health Data: Challenges and Opportunities
报告人：林希虹 哈佛大学
时间：2020.01.03(周五)上午10:30-11:30
地点：紫金港校区管理学院行政楼14楼1417报告厅
摘要： Massive data from genome, exposome, and phenome are becoming available at a rapidly increasing rate with no apparent end in sight. Examples include Whole Genome Sequencing data, smartphone data, wearable devices, Electronic Health Records and biobanks. The emerging field of Health Data Science presents statisticians, computer scientists and informaticians, and quantitative scientists, with many exciting research and training opportunities and challenges. Success in health data science requires scalable statistical inference integrated with computational science, information science and domain science. In this talk, I discuss some of such challenges and opportunities, and emphasize the importance of incorporating domain knowledge in health data science method development and application. I illustrate the key points using several use cases, including analysis of data from large scale Whole Genome Sequencing (WGS) association studies, integrative analysis of different types and sources of data using causal mediation analysis, reproducible and replicable research, and cloud computing. I will discuss the data and analytic sources and tools being developed in the ongoing large scale whole genome sequencing studies of the NHGRI Genome Sequencing Program and the NHLBI Trans-Omics Precision Medicine Program of over 500,000 genomes.
欢迎广大师生踊跃参加!
联系人：蒋杭进（jianghj@zju.edu.cn）
报告人简介：
林希虹，哈佛大学生物统计系教授。她于1994年获华盛顿大学生物统计系博士。1994-1999年任密西根大学生物统计系助理教授，教授。2002年获美国公共卫生学会颁发的Mortimer Spiegelman奖。2005年起任哈佛大学生物统计系教授。2006年获统计界最高奖COPSS奖，2007年获美国国家卫生研究院的杰出成就奖，并当选美国数理统计学会会士。2010年成为由北美五大统计协会组成的最高统计协会考普斯委员会主席。2018年10月，当选为美国国家医学院院士。